Investigation of electrophoretic loading and enhanced mechanical properties of hydrogels for delivery of therapeutic proteins

Hydrogels, which are three-dimensional crosslinked hydrophilic polymers, have been used and studied widely as vehicles for drug delivery due to their good biocompatibility. Traditional methods to load therapeutic proteins into hydrogels have some disadvantages. Biological activity of drugs or proteins can be compromised during polymerization process or the process of loading protein can be really timeconsuming. Therefore, different loading methods have been investigated. Based on the theory of electrophoresis, an electrochemical gradient can be used to transport proteins into hydrogels. Therefore, an electrophoretic method was used to load protein in this study. Chemically and radiation crosslinked polyacrylamide was used to set up the model to load protein electrophoretically into hydrogels. Different methods to prepare the polymers have been studied and have shown the effect of the crosslinker (bisacrylamide) concentration on the protein loading and release behaviour. The mechanism of protein release from the hydrogels was anomalous diffusion (i.e. the process was non-Fickian). The UV-Vis spectra of proteins before and after reduction show that the bioactivities of proteins after release from hydrogel were maintained. Due to the concern of cytotoxicity of residual monomer in polyacrylamide, poly(2-hydroxyethyl- methacrylate) (pHEMA) was used as the second tested material. In order to control the pore size, a polyethylene glycol (PEG) porogen was introduced to the pHEMA. The hydrogel disintegrated after immersion in water indicating that the swelling forces exceeded the strength of the material. In order to understand the cause of the disintegration, several different conditions of crosslinker concentration and preparation method were studied. However, the disintegration of the hydrogel still occurred after immersion in water principally due to osmotic forces. A hydrogel suitable for drug delivery needs to be biocompatible and also robust. Therefore, an approach to improving the mechanical properties of the porogen-containing pHEMA hydrogel by introduction of an inter-penetrating network (IPN) into the hydrogel system has been researched. A double network was formed by the introduction of further HEMA solution into the system by both electrophoresis and slow diffusion. Raman spectroscopy was used to observe the diffusion of HEMA into the hydrogel prior to further crosslinking by ã-irradiation. The protein loading and release behaviour from the hydrogel showing enhanced mechanical property was also studied. Biocompatibility is a very important factor for the biomedical application of hydrogels. Different hydrogels have been studied on both a three-dimensional HSE model and a HSE wound model for their biocompatibilities. They did not show any detrimental effect to the keratinocyte cells. From the results reported above, these hydrogels show good biocompatibility in both models. Due to the advantage of the hydrogels such as the ability to absorb and deliver protein or drugs, they have potential to be used as topical materials for wound healing or other biomedical applications.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

Background The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. Conclusion A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Two SusD-like proteins encoded within a polysaccharide utilisation locus of an uncultured ruminant bacteroidetes bind strongly to cellulose

We demonstrate that two characteristic Sus-like proteins encoded within a Polysaccharide Utilisation Locus (PUL) bind strongly to cellulosic substrates and interact with plant primary cell walls. This shows associations between uncultured Bacteroidetes-affiliated lineages and cellulose in the rumen, and thus presents new PUL-derived targets to pursue regarding plant biomass degradation.